import wandb
import torch
import torch.nn as nn
import torch.nn.functional as F
from tensorboardX import SummaryWriter

wandb.init(tensorboard=True)


class ConvNet(nn.Module):
    def __init__(self):
        super(ConvNet, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)


writer = SummaryWriter()
net = ConvNet()
wandb.watch(net, log_freq=2)
for i in range(10):
    output = net(torch.ones((64, 1, 28, 28)))
    loss = F.mse_loss(output, torch.ones((64, 10)))
    output.backward(torch.ones(64, 10))
    writer.add_scalar("loss", loss / 64, i+1)
    writer.add_image("example", torch.ones((1, 28, 28)), i+1)
writer.close()
